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Huawei H13-321_V2.5 Exam Syllabus Topics:
| Section | Objectives |
| AI Fundamentals | - Introduction to Artificial Intelligence
- AI Development Lifecycle
- Common AI Use Cases in Industry
|
| Deep Learning | - Neural Network Fundamentals
- Model Training and Optimization
- CNN and RNN Architectures
|
| Data Processing | - Data Collection and Cleaning
- Data Labeling and Preparation
- Feature Engineering
|
| Huawei AI Ecosystem Tools | - Huawei Cloud AI Services
- MindSpore Framework Basics
- AI Development Toolchain
|
| AI Application Development (EI) | - Enterprise Intelligence (EI) Concepts
- AI Service Integration
- Building AI Applications
|
| Machine Learning | - Unsupervised Learning
- 1. Clustering Methods
- 2. Dimensionality Reduction
- Supervised Learning
- 1. Classification Algorithms
- 2. Regression Algorithms
|
| Model Deployment and Operations | - Inference Services
- Model Deployment Strategies
- Monitoring and Maintenance
|
| Model Development with Huawei ModelArts | - Training Models on ModelArts
- ModelArts Platform Overview
- AutoML Capabilities
|
Huawei HCIP-AI-EI Developer V2.5 Sample Questions:
1. In an HSV color space, H is for hue, S is for saturation, and V is for value. Which of the following statements about the HSV color space are true?
A) Value is a measure of brightness. The image brightness can be enhanced by processing the V component of the HSV color space.
B) The HSV color space perceives colors differently from human eyes, so it is not suitable for image segmentation or color analysis.
C) Hue indicates the basic color attributes, such as red, green, and blue.
D) Saturation describes how vivid the color is. The lower the saturation, the closer the color is to gray. The higher the saturation, the more vivid the color.
2. Which of the following applications are supported by ModelArts ExeML?
A) Anomalous sound detection in production or security scenarios
B) Dress code conformance monitoring in campuses
C) Automatic offering classification
D) Predictive maintenance of manufacturing equipment
3. Which of the following statements are true about the differences between using convolutional neural networks (CNNs) in text tasks and image tasks?
A) CNNs are suitable for image tasks, but they perform poorly in text tasks.
B) Color image input is multi-channel, whereas text input is single-channel.
C) For CNN, there is no difference in handling text or image tasks.
D) When the CNN is used for text tasks, the kernel size must be the same as the number of word vector dimensions. This constraint, however, does not apply to image tasks.
4. In an image preprocessing experiment, the cv2.imread("lena.png", 1) function provided by OpenCV is used to read images. The parameter "1" in this function represents a --------- -channel image. (Fill in the blank with a number.)
5. The jieba ------() method can be used for word segmentation.
Solutions:
Question # 1 Answer: A,C,D | Question # 2 Answer: A,B,C,D | Question # 3 Answer: B,D | Question # 4 Answer: Only visible for members | Question # 5 Answer: Only visible for members |